Fisheries Management and Ocean Observations

Dave Checkley

Scripps Institution of Oceanography

dcheckley@ucsd.edu

Acknowledgments

Steven Bograd NOAA Fisheries USA

Nick Caputi CSIRO Australia

Dave Demaster NOAA Fisheries USA

Alastair Hobday CSIRO Australia

Beth Fulton CSIRO Australia

Pierre Fréon IRD France

Renato Guevara IMARPE Peru

Anne Hollowed NOAA Fisheries USA

Brian MacKenzie Danish Technical University Denmark

Lorenzo Motos AZTI Spain

Francisco Neira TAFI Australia

Yoshioki Oozeki NFRI Japan

Ian Perry Fisheries & Oceans Canada

Bill Peterson NOAA Fisheries USA

Benjamin Planque University of Tromso Norway

Jeff Polovina NOAA Fisheries USA

Ryan Rykaczewski GFDL Princeton USA

Svein Sundby IMR Norway

Carl van der Lingen Marine & Coastal Management South Africa

Yoshiro Watanabe ORI Japan

George Watters NOAA Fisheries USA

Main Points

• Ecosystem services, maximum sustainable yield, and rebuilding overexploited stocks are primary goals of fisheries management

• Use of ocean observations in fisheries management is in its infancy

• The next 10 years will see a large increase in the use of ocean observations for fisheries management through the enhancement of sensors, platforms, integrated observing systems, data delivery and use, and models

• Enhanced collaboration among the observing and fisheries communities is essential and should be a goal

OceanObs’09

Is ocean observing critical to fisheries management in 2009?

OceanObs’09

Is ocean observing critical to fisheries management in 2009?

No - only in a very few cases

Fisheries

Removal of fish from the sea by humans

• Fisheries target single species populations – ‘stocks’

• Fishers, not fish, are managed (Ian Perry)

• Climate and fishing together affect fish populations

World Marine Fisheries ProductionC

aptu

re F

isherie

s

Aquaculture

×××

☐☐

☐

(Brander 2007)

World Marine Fisheries ProductionC

aptu

re F

isherie

s

Aquaculture

×××

☐☐

☐

(Brander 2007)

Capture Fisheries

World Marine Fisheries ProductionC

aptu

re F

isherie

s

Aquaculture

×××

☐☐

☐

(Brander 2007)

Aquaculture

Capture Fisheries

World Fish Landings – Top 10

Peruvian anchoveta 7 007 157 tons

Alaska pollock 2 860 487

Skipjack tuna 2 480 812

Atlantic herring 2 244 595

Blue whiting 2 032 207

Chub mackerel 2 030 795

Chilean jack mackerel 1, 828 999

Japanese anchovy 1, 656 906

Largehead hairtail 1 587 786

Yellowfin tuna 1 129 415

(FAO)

Observations of Last Three Days

(JCOMM)

Floats, buoys, and ships – not satellites

17% of Global Marine Fish Landings

(Stobutski et al. 2006)

Fisheries Management Objectives

Greatest overall benefit, including ecosystem services

Maximum Sustainable Yield, reduced by other factors

Rebuilding if overfished

(Magnuson-Stevens Reauthorization Act of 2007)

Management

ObserveModel

Inform GovernIndicators

Canonical Management

ObserveModel

Inform GovernIndicators

Fishery Dependent Data

(e.g., fish size, age, and abundance from landings)

Ideal Management

ObserveModel

Inform GovernIndicators

Fishery Dependent Data and Ocean Observations

California Sardine

Varies with climate (PDO)

on decadal scale

Prefers warm conditions

1950 2000

Sp

aw

nin

g S

tock B

iom

ass

Recru

itme

nt

warm warmcold

(NOAA

Fisheries)

California Sardine

3-year running mean of

SIO Pier temperature

used to determine

fraction of sardine

biomass fished

Scripps Pier

Decision Rule

2000 2007

16° 17°C0

10

20

Pe

rce

nt

(NOAA Fisheries)

Turtle By-Catch Reduction

(Duke U)Problem

By-catch of loggerhead sea turtles in the North Pacific

longline fishery for swordfish

Solution

Satellite tags and remote sensing define sea turtle habitat

SST and altimetry used to map habitat

Weekly advisory product to forecast the zone with the

swordfish fishing ground which has the highest probability

of interactions between sea turtles and longliners

Turtle By-Catch Reduction

(Polovina)

Bluefin Tuna By-Catch Reduction

Analysis and habitat prediction tools

Biological Data

(tags)

Habitat Preferences

Physical Data (near-real time distribution of environment)

Ocean Model (Bluelink)

Habitat Prediction Maps

Management Support

(sustainable use)(Hobday, CSIRO)

Objective: Reduce BFT by-catch in tropical tuna longline fishery

Bluefin Tuna By-Catch Reduction

(Hobday. CSIRO)

Habitat Index Habitat Management Zones

Biweekly: SST & altimetry used with habitat prediction

model then management meets to zone the area

Work backwards…

ObserveModel

Inform GovernIndicators

Work backwards…

ObserveModel

Inform GovernIndicators

Governance

Management Options

Catch control

Total catch (race to fish)

Catch shares (rights-based fishing)

Effort control

Time limits

Vessel or gear restrictions

Area (Marine Spatial Management)

Affected by: Natural science, socioeconomics, politics

Population & Ecosystem Models

Deterministic

Limitation: fish behavior

(“like unmanageable children”…Oozeki-san)

Example: NEMURO

Statistical

Assumes past behavior

Non-linear, short-term

Indexes

Single number indicating the state of a fish stock, fishery, ecosystem, or environment

Physical: SOI, PDO, NPGO, NPI, NAO, IOD, SIO Pier Temp

Biological: CPUE

Mean trophic level (Pauly)

Ocean Production Index – fraction released salmon returning to spawn (Peterson)

Indicator (sentinel) species – e.g., predatory seabirds (gannets diving on sardine) (van der Lingen)

Maximum species yield, food-web based yield, species-diversity based yield (Gifford and Steele)

Physical Data

Met data (e.g., Tair, wind, BP, humidity)

Light

Temperature, salinity, pressure

Stratification, mixing

u, v, w

Turbulence (ε)

Sea level height

Chemical Data

O2

pH

pCO2

Nutrients

Chl a

Biological Data

Phytoplankton and zooplankton

Fish

Birds, Reptiles, Mammals

Distribution and abundance

Migrations

Interactions (feeding and predation – gut contents)

Developmental stages: egg, larva, juvenile, and adult

Size spectra

Socioeconomic Data

Costs

Markets

Trading

Employment

Ecosystem services

Integrated Ecosystem Assessment

Formal synthesis and quantitative analysis of information on relevant natural and socioeconomic factors, in relation to specified ecosystem management objectives

Levin et al. 2009

End-to-End Fishery Model

Atlantis

19

systems

(Beth Fulton, CSIRO)

New Sensors

Acoustics

Active: Multibeam (3D from moving ship) acoustics

Passive: marine mammals, anthropogenic

Imaging (Sieracki CWP)

Molecular

Genetics

Proteomics

Holy Grail: Rapid, accurate, automated species identification and assessment

Platforms

Satellites SST, SLH, color, winds, salinity

Ships Station grids (e.g., CalCOFI)

Underway sampling (e.g., CPR, CUFES, MVP, SEASOAR)

VMS – (fishing) vessel monitoring systems

Lagrangian floats, gliders, AUVs

Eulerian moorings (buoys, subsurface profiling winches)

Animals tagging (archival, satellite)

bio-logging (Boehme, Costa CWPs)

acoustic listening networks (e.g., POST; O’Dor CWP)

CWPs: Handegard, Koslow, Larkin, Malone

Observing Challenges

• No silver bullet (Beth Fulton)

• Timely and open access to data

• Sampling of aggregated (patchy) distributions

• Time resolution (e.g., spring bloom, spawning, phenology)

• Species interactions (feeding, predation)

• Relating physics, chemistry, and biology – scale mismatches

- the need for comparable data

• Socioeconomics – human dimensions

• Risk and uncertainty

• Participation: stakeholders, scientists, managers

• Coastal observing and capacity building

The Future

OceanObs’19 - Predictions

• Yes - ocean observations are critical to fisheries management

• Developing, as well as developed, countries use ocean observations for fisheries management

• Climate effects on fisheries will be much more apparent and ocean observing has contributed to detecting and understanding these, including rising, warming, deoxygenation, and acidification

• Progress on the understanding of the effects of climate and fishing on fish stocks, allowing NFP (Numerical Fisheries Prediction)

CWPs: Feely, Forget (SAFARI)

Main Points

• Ecosystem services, maximum sustainable yield, and rebuilding overexploited stocks are primary goals of fisheries management

• Use of ocean observations in fisheries management is in its infancy

• The next 10 years will see a large increase in the use of ocean observations for fisheries management through the enhancement of sensors, platforms, integrated observing systems, data delivery and use, and models

• Enhanced collaboration among the observing and fisheries communities is essential and should be a goal